卵巢癌通常被认为是最致命的妇科癌症，因为它往往在晚期才被诊断出来，导致治疗选择有限且预后较差。卵巢癌的治疗面临诸多挑战，包括快速转移、遗传因素、生育史等。因此，需要对卵巢癌进行及时、准确的诊断，以便实施有效的治疗计划，为所有受 OC 影响的患者提供帮助。他们需要的关心和支持。该CCLSTM模型建议分为四个基本阶段，包括预处理、特征提取、特征选择和检测。最初，使用改进的两步数据标准化对输入数据进行预处理。随后，从归一化数据中提取统计特征、修正熵、原始特征和互信息等特征。接下来，获得的特征经过改进的基于排序的递归特征消除方法（IR-RFE）来选择最合适的特征。最后，所提出的 CCLSTM 模型将所选特征作为输入并提供最终的检测结果。此外，通过使用各种分析进行彻底评估来检查所提出的 CCLSTM 技术的性能。此外，CCLSTM 方案的灵敏度值为 0.948，而ALO-LSTM、ALOCNN、Bi-GRU、LSTM、RNN、KNN、CNN、DCNN 的灵敏度等级分别为 0.808、0.893、0.829、0.851、0.765、0.872、0.893。 LSTM 技术的加入产生了一种卵巢癌检测技术，与其他现有策略相比，该技术更加准确和一致。版权所有 © 2024。由 Elsevier B.V. 出版。

Ovarian cancer is often considered the most lethal gynecological cancer because it tends to be diagnosed at an advanced stage, leading to limited treatment options and poorer outcomes. Several factors contribute to the challenges in managing ovarian cancer, namely rapid metastasis, genetic factors, reproductive history, etc. This necessitates the prompt and precise diagnosis of ovarian cancer in order to carry out efficient treatment plans and give patients who are all impacted by OC the care and support they need.This CCLSTM model is suggested under four essential stages including preprocessing, feature extraction, feature selection and detection. Initially, the input data is preprocessed using Improved Two-step Data Normalization. Subsequently, features such as statistical, modified entropy, raw features and mutual information are extracted from the normalized data. Next, obtained features undergo the Improved Rank-based Recursive Feature Elimination method (IR-RFE) to select the most suitable features. Finally, the proposed CCLSTM model takes the selected features as input and provides a final detection outcome.Furthermore, the performance of the proposed CCLSTM technique is examined through a thorough assessment using diverse analyses Additionally, the CCLSTM schemes show a sensitivity value of 0.948, whereas the sensitivity ratings for ALO-LSTM + ALOCNN, Bi-GRU, LSTM, RNN, KNN, CNN, and DCNN are 0.808, 0.893, 0.829, 0.851, 0.765, 0.872, and 0.893, respectively.In the end, the development of CNN and the addition of LSTM technique have produced an ovarian cancer detection technique that is more accurate and consistent compared to other existing strategies.Copyright © 2024. Published by Elsevier B.V.